Atypical antipsychotics to treat the neuropsychiatric symptoms of dementia
Bibliographic record
Abstract
Neuropsychiatric symptoms are common in older adults with dementia and can be associated with a rapid decline in cognitive and functional status. This article reviews the current literature supporting the use of atypical antipsychotic medications in this population. Among the currently available atypical antipsychotics, risperidone and olanzapine have been the most widely studied in double-blind, randomized, placebo-controlled clinical trials. Despite the common use of other atypical antipsychotic medications, their efficacy and safety in older adults with dementia has not been as extensively studied. Some controversy surrounds the use of atypical antipsychotic agents in older adults with the suggestion that they may increase the incidence of stroke or even death. Despite the potential for increased risk of harm from the use of these medications, atypical antipsychotics are often effective in treating troublesome neuropsychiatric symptoms refractory to other treatments. Whenever possible, these atypical antipsychotic drug treatments should be combined with non-pharmacological treatments to limit the need and dose of antipsychotic drugs and constant monitoring for potential harms should be maintained. The choice of which atypical antipsychotic agent can be guided by the nature and severity of the target symptom and the medication least likely to cause harm to the patient.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".